import torch
import torch.nn as nn

class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        else:
            self.shortcut = nn.Identity()

    def forward(self, x):
        identity = self.shortcut(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)

        return out

class RegNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(RegNet, self).__init__()
        self.in_channels = 32
        self.layer0_conv1 = nn.Conv2d(3, self.in_channels, kernel_size=3, stride=2, padding=1, bias=False)
        self.layer0_bn1 = nn.BatchNorm2d(self.in_channels)
        self.layer0_relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels, stride=1))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.layer0_conv1(x)
        x = self.layer0_bn1(x)
        x = self.layer0_relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        result = {'output': x}
        return result

def regnetY_800mf(num_classes):
    return RegNet(BasicBlock, [1, 1, 1, 1], num_classes=num_classes)
